Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved
its effectiveness in bioinformatics due to its capacity to produce local instead of global …

Improving the performance of classifiers using pattern mining techniques has been an active
topic of data mining research. In this work we introduce the recent temporal pattern mining …

We study the problem of learning classification models from complex multivariate temporal
data encountered in electronic health record systems. The challenge is to define a good set …

Sequential pattern mining is a computationally challenging task since algorithms have to
generate and/or test a combinatorially explosive number of intermediate subsequences. In …

Human activity recognition is an important task which has many potential applications. In
recent years, researchers from pervasive computing are interested in deploying on-body …

Sequence classification is an important task in data mining. We address the problem of
sequence classification using rules composed of interesting patterns found in a dataset of …

Quantitative intelligence based traditional data mining is facing grand challenges from real-
world enterprise and cross-organization applications. For instance, the usual demonstration …

The random forest classifier is widely used in different fields due to its accuracy and
robustness. Since its invention, the random forest algorithm is naturally developed for multi …

The discovery of discriminative patterns from high-dimensional data offers the possibility to
learn from informative subspaces and pattern-centric features, paving the way to associative …

This work proposes a pattern mining approach to learn event detection models from
complex multivariate temporal data, such as electronic health records. We present recent …